• Sophia S Goller, Sarah C Foreman, Jon F Rischewski, Jürgen Weißinger, Anna-Sophia Dietrich, David Schinz, Robert Stahl, Johanna Luitjens, Sebastian Siller, Vanessa F Schmidt, Bernd Erber, Jens Ricke, Thomas Liebig, Jan S Kirschke, Michael Dieckmeyer, and Alexandra S Gersing.
• Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany. sophia.goller@med.uni-muenchen.de.
• Eur Spine J. 2023 Dec 1; 32 (12): 431443204314-4320.

PurposeTo assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs).MethodsA total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs.ResultsSkewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs.ConclusionThree-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.© 2023. The Author(s).

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